@inbook{4b2f020fa5434af9a855bf342fe29ca0,
title = "Two Bayesian treatments of the n-tuple recognition method",
abstract = "Two probabilistic interpretations of the n-tuple recognition method are put forward in order to allow this technique to be analysed with the same Bayesian methods used in connection with other neural network models. Elementary demonstrations are then given of the use of maximum likelihood and maximum entropy methods for tuning the model parameters and assisting their interpretation. One of the models can be used to illustrate the significance of overlapping n-tuple samples with respect to correlations in the patterns.",
keywords = "Bayes methods, maximum entropy methods, neural nets, pattern recognition, Bayesian treatments, maximum entropy, maximum likelihood, model parameters, n-tuple recognition, neural network models, probabilistic interpretations",
author = "Richard Rohwer",
note = "{\textcopyright}1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; Proc. IEE 4th International Conf. on Artificial Neural Networks (publication 409) ; Conference date: 26-06-1995 Through 26-06-1995",
year = "1995",
month = jun,
day = "26",
doi = "10.1049/cp:19950549",
language = "English",
isbn = "0852966415",
series = "IEE conference publication",
publisher = "IEEE",
pages = "171--176",
booktitle = "Fourth International Conference on Artificial Neural Networks, 1995",
address = "United States",
}